Overview
The marketing team generates 500 leads per month. Sales calls all of them in order received. By month end, 18 converted — a 3.6% conversion rate. But analysis shows: 12 of the 18 conversions came from leads that visited the pricing page, downloaded a case study, AND came from companies with 50-500 employees. These leads had a 24% conversion rate. The other 482 leads had a 1.2% conversion rate. Without scoring, sales spent equal time on 24%-probability and 1.2%-probability leads — wasting 80% of their effort on leads unlikely to convert.
Predictive Lead Scoring assigns a conversion probability to each lead based on firmographic attributes (company size, industry, geography), behavioral signals (page visits, content downloads, email engagement), and engagement patterns (frequency, recency, depth) — enabling sales to prioritize the highest-probability leads and marketing to focus nurture efforts on leads not yet ready.
What you get: - Multi-signal scoring model with firmographic and behavioral inputs - Score calculation with weighted feature importance - Score-to-action mapping (hot, warm, nurture, disqualify) - Model training and validation methodology - Score calibration and decay rules - Performance measurement (does the score predict conversion?)
Built for: B2B sales teams where leads outnumber sales capacity — where scoring directs limited sales effort toward the leads most likely to convert, increasing conversion rate and reducing wasted outreach.